Stephen Downes is a prolific writer. If you follow his work at OLDaily or on Half an Hour, you’re well aware of this. He covers an extremely broad territory: technology, learning, society, politics (sometimes a bit veiled, but generally not far below the surface), and philosophy.

Learning is the creation and removal of connections between the entities, or the adjustment of the strengths of those connections. A learning theory is, literally, a theory describing how these connections are created or adjusted. In this book I describe four major mechanisms: similarity, contiguity, feedback, and harmony. There may be other mechanisms, these and others may work together, and the precise mechanism for any given person may be irreducibly complex.

Stephen doesn’t make any apologies for the length of the ebook in stating that a formally structured book “would be sterile, however, and it [the ebook he has posted] feels more true to the actual enquiry to stay true to the original blog posts, essays and presentations that constitute this work”

I personally would like to see Stephen produce a succinct text. Until he does so, students (and others) have a valuable resource in tracking and citing his work in networks, MOOCs, meaning, groups & networks, semantics, and more. Simply being able to point to and cite a particular page will be helpful for students…Thanks Stephen!

The Change MOOC has been running since September of 2011. We’ve had the pleasure, in the past 30+ weeks, of many outstanding discussions. The archives of activity/readings/weeks are available on the main MOOc site.

Each week, different facilitators share readings and resources that they deem to be most reflective of their work and their passion.

My week is on sensmaking and analytics.

At first glance, sensemaking and analytics seem antagonistic. Sensemaking involves social processes…whereas analytics are algorithmically-driven. MOOCs are distributed systems of interaction and content. The traditional approach to courses – pre-packaged before learners arrive – is upended in a MOOC. The hyper-fragmentation of content and interaction presents problems for educators and learners: How do we make sense of what’s happening? How do we develop a coherent view of the many, many topics that comprise a MOOC? How do we re-create a centre that shares the bounding elements of a course, but is based on the networked centre-less structure of the internet?

Sensemaking

Sensemaking is an activity that individuals engage in daily in response to uncertainty, complex topics, or in changing settings. Much like with the earlier discussion of the term “information”, sensemaking is a term in common use but with limited agreement on what it precisely means. Researchers argue that “[n]o single, unambiguous answer can be given…for sense-making theory has several meanings depending on the disciplinary or paradigmatic position of the speaker” (Kari 1998: 1).

In contrast to decision-making models in crisis situations, Weick, Sutcliffe, and Obstfeld (2005: 415) promote a narrative model of sensemaking. They argue that sensemaking is “not about truth and getting it right. Instead it is about continued redrafting of an emerging story so that it becomes comprehensible.” Weick’s sensemaking model emphasizes non-linearity, and pattern recognition. The importance of pattern recognition is consequential in that it integrates the expertise of individuals with narratives of coherence. Sensemaking is an effort “to create order and make retrospective sense of what occurs” (Weick 1993: 635).

Nowhere is the emphasis on dialogue more precise than in the work of Brenda Dervin (2003). The Dervin Sense-Making Methodology, dating back to the early 1970s, “is proposed as an alternative to approaches based on traditional transmission models of communication” (Dervin 2003: 6). Dervin (2003: 238) uses the metaphors of “situation” “outcomes”, and “gaps”, “moving across time and space, facing a gap, building a bridge across the gap, and then constructing and evaluating the uses of the bridge.”

Sensemaking and the process of learning are related, but each has distinct constructs (Schwandt 2005). Learning emphasizes time for consideration, reflection, and integration, whereas sensemaking is “swift and hasty as opposed to reflective” (Schwandt 2005: 189). In sensemaking, individuals understand a problem that “they face only after they have faced it and only after their actions have become inextricably wound into it” (Weick 1988: 306). In contrast, formal learning often occurs within a construct of increasing the capacity of an individual to act, instead of situation-specific sensemaking activities.

With the breadth of the topic of sensemaking, and its intuitive feel and common use, it is unsurprising that numerous definitions exist. A sampling of definitions include:

- “Sensemaking is finding a representation that organizes information to reduce the cost of an operation in an information task” (Russell et al. 1993: 272).
- “[S]ensemaking is a motivated, continuous effort to understand connections . . . in order to anticipate their trajectories and act effectively” (Klein et al. 2006: 71).
- “Sensemaking is about labeling and categorizing to stabilize the streaming of experience” (Weick et al. 2005: 411) and differs from decision making in its focus on “contextual rationality” (Weick 1993: 636).
- Sensemaking involves individual’s attempting to “negotiate strangeness” (Weick 1993: 645). Failures in these settings occurs when “[f]rameworks and meanings [destroy] rather than [construct] one another” (Weick 1993: 645).

Sensemaking, then, is essentially the creation of an architecture of concept relatedness, such as placing “items into frameworks” (Weick 1995:6) and continually seeking “to understand connections” (Klein et al. 2006: 71). Sensemaking occurs in many facets of personal and organizational life, including crisis situations, routine information seeking, research, and learning. Individuals engage in nebulous problem solving without a clear path daily: a parent raising a child, an employee starting a new job, a doctor without a clear diagnosis for a patient, a master’s research student, and so on.

Analytics

My interest in analytics is driven by my views on learning as a connection-making process. Through analytics we are able to trace connections, understand how they are formed, the nature of exchanges between people, and the impact of those connections. The data-trails that are created in our daily interactions online and with others form the basis of analytics in learning. The field, however, is still developing and new approaches to analysis, algorithms, and tools are quickly emerging.

They are all examples of sensemaking artifacts. Teaching and learning in social and technical networks is difficult (at first) because many of the routines and activity markers from traditional courses and classrooms are not present. There is no centre, no one space where everything is held. Conversations are fragmented. The teacher’s coherence or subject views aren’t “duplicated” by students. Of course some basic knowledge elements exist, but the way we come to know them in networks is different from the process of coming to know them in classrooms.

When learners enter complex information settings, the first experience is one of disorientation. “Where do I go?” “Where can I find what I need?” “Who else is here?” As a learner orients herself, she begins to form connections with a few others, resulting in sub-networks often based on some similarity (same country, similar interests, previous connection). In our work in open online courses, we’ve found consistent patterns emerging as learners interact with each other and with information. Through joint processes of sensemaking and wayfinding – see presentation below – learners begin exploring and negotiating the domain of knowledge. In the process, they produce artifacts, such as the images posted above. Artifacts can include a blog post, an image, a video, a podcast, a live performance – basically anything that allows an individual to express how they’ve come to understand something.

These artifacts serve two roles:

1. They reflect the sensemaking activity that the individual has experienced – how he connected different concepts within a course or how he came to understand the relationship between different entities.

2. They are a sensegiving tool. When learners are transparent in their learning through the production and sharing of artifacts, they teach others.

Sensemaking artifacts are valuable in that learners use them to self-organize around important ideas, negotiate the scope of a topic, correct each other, and curate key ideas.

I often grapple with the question: “if we designed education today, what would it look like?”. Would it look like our existing classrooms? Textbooks? Libraries? Or would it look more like the internet? What roles would teachers play? Or learners? What would “teaching” look like if we had a system that jettisoned the legacy baggage of our current education system?

We don’t yet know how to answer these questions – there are too many unsettled trends and change pressures to strongly assert the future of education. However, we sometimes get glimpses of where the system is trending. At its core, digital technologies change how people relate to each other and how information is created and shared. These trends influence the power structures in classroom or online settings. One such power change centres on the learner: she has more power today than ever before, requiring both educators and institutions to rethink what they do for her and what she can do for herself. Sensemaking artifacts reflect this power shift: learners can self-organize and guide each other, rather than simply walking established knowledge paths created by educators and designers. Each artifacts serves to “re-centre” the conversation around the sensemaking actions of learners. In this regard, sensemaking artifacts are in competition with the planned curriculum (learning content) for the attention of students and teachers.

Obviously, there are things to consider – namely, the need for educator intervention when it appears that students aren’t “fact checking” each other. Overall, however, sensemaking artifacts are another node in the learning model that distributes control and power away from the institution and the teacher and moves it (power/control) into the networks formed by students.

This week, for #change11, Dave Cormier is facilitating a discussion on rhizomatic learning. I’ve been aware of Dave’s thinking on this topic since his article in Innovate (.pdf). I was the guest editor of this journal issue and Dave’s article is the one that generated the most interest and still continues to resonate with many people. It certainly resonates with me.

One aspect of Cormier’s work on rhizomatic learning that he has not fully resolved is how rhizomes are related to networks. Periodically, he expresses rhizomes as an alternative to the network model. For example, in his discussion on edtechweekly, he suggests that networks are too structured and lack the organic attributes of rhizomes. On other occasions, he has raised concerns about the knowledge or epistemological underpinnings of networked learning (connectivism). I sense a lingering discomfort with networks in Cormier’s view of knowledge and learning. He hasn’t tackled this discomfort directly. Perhaps he is trying to be polite. Or perhaps he’s still thinking through the nature of that discomfort. Personally, I’d like to see him explore in more detail where the networked learning model fails in contrast with rhizomes.

Rhizomes are appealing for several reasons. First, they share the decentralized attributes of networks. There is no centre. Second, rhizomes are organic, they’re living and adaptive. A rhizomatic structure today will be different from a structure that will exist in a few months. Third, each part of a rhizomatic structure is capable of producing a new plant, propagating and replicating itself without central control. Fourth, in contrast with an architected structure such as a road system, rhizomes are not artificially bounded. They continually grow, extend, and develop. It’s easy to see how the decentralized, organic, adaptive, self-replicating, and unbounded characteristics of rhizomes are appealing as metaphors for learning and knowledge.

I’m not sure whether Cormier views rhizomes as a metaphor for learning or whether he is bolder in his claims. Does he think that learning is *like* a rhizome? Or is he making a bolder statement in saying that learning *is* a rhizome? In the blog post I referenced above, he states that rhizomes are “a way of thinking about learning”. If this assertion is central in his work, then I largely agree with Cormier.

However, when rhizomes are considered in contrast with networks, I find the rhizome model begins to lose some appeal. The greatest weakness I see with the rhizome model is that rhizomes do nothing new. They only make more of what already exists. And they can only make more of themselves. This is the opposite of diversity – it is extreme monoversity (it’s not a word, I checked, but it works for me). While rhizomes are diverse in shape and structure – growing, adapting, sprouting, replicating – they are not diverse in substance – i.e. rhizomes do not morph into new organic entities.

In his edtechweekly podcast, Cormier criticizes networks as being “duplicatable”. If someone has a successful network, she is tempted to say “this is how you create your own network”. Suddenly, the network becomes mechanistic. Cormier doesn’t like that. Neither do I. However, networks need not be designed in order to duplicate structure. Networks are organic – consider food webs, ecosystems, and the architecture of the brain. I don’t accept the argument that rhizomes are organic and that networks are not.

Cormier doesn’t seem to appreciate the descriptive attributes of networks – i.e. that we can describe certain network attributes by formula – power laws, propagation of influence, in-degrees and out-degrees. Personally, I think the descriptive and analytic capacities that have been applied to networks are tremendously positive. From the outbreak of cholera to the structure of terrorist cells to the development of the internet, being able to understand, often mathematically, the structure of networks enables us to react to and even build on that structure. It is because we can measure and understand how messages move through networks, the role of hubs, the need for amplification of messages, etc., that we are able to build the internet. The “definiteness” of networks is what gives them their relevance and power in our society today.

Together with Stephen Downes, Cormier and I have had long running discussions about objectivism, subjectivism, knowledge, learning, and a raft of other related concepts. I’m of that rare breed that still believes structure can be good and that some level of objectivity can exist in some situations. Inevitably, when this conversation begins, I find myself arguing with Downes and Cormier. It has never been resolved. And I don’t think it ever will be.

My network view of knowledge is simple: entities (broadly defined as well, anything: people, a chemical substance, information, etc) have attributes. When entities are connected to other entities, different attributes will be activated based on the structure of those connections and the nature of other entities that are being connected. This fluidity of attribute activation appears to be subjective, but in reality, is the contextual activation of the attributes of entities based on how they are related to other entities. Knowledge then is literally the connections that occur between entities.

I don’t see networks as a metaphor for learning and knowledge. I see learning and knowledge as networks. In global, digital, distributed, and complex settings, a networked model of learning and knowledge is critical. Most disciplines in society have become too specialized to function in isolation. Global problems are too intractable to be tackled by any structure other than networks. Generalists have given way to connected specialization (as evidenced in the identification process of the corona virus (SARS)). Everything – form fixing my car, to my morning coffee, to my research, to my mobile phone, to healthcare – is a function of connected specialization. Novelty and innovation arises when we collide ideas or specialties that previously had not been brought in relation to one another.

Much of the world that we live in today can be explained through networks, including education, learning, research, and knowledge development. I have not read Deleuze, though it is on my agenda for my trip to Croatia this weekend, but I don’t see rhizomes as possessing a similar capacity (to networks) to generate insight into learning, innovation, and complexity. Terry Anderson describes a sense of alienation with the rhizomatic learning and states that: “If we really want to CHANGE systems, we have to insure that we don’t grow as rhizomes, reproducing clones of ourselves or establishing gardens in which only certain types of weeds can flourish.”

Rhizomes then, are effective for describing the structure and form of knowledge and learning – bumpy, lumpy, organic, and adaptive. But they fail to describe how learning occurs, how novelty happens, and how a rhizome becomes more than a replication of itself. Rhizomes can be a helpful way to think about curriculum, to think about how we develop educational content when we are connected (dang networks again) to one another and to information sources. However, beyond the value of describing the form of curriculum as decentralized, adaptive, and organic, I’m unsure what rhizomes contribute to knowledge and learning.

This post grapples with an idea that I’m still coming to understand, but that feels important: namely, who participates in open online courses, what are the elements of privilege that we overlook in planning and running course, who benefits, and why?

When we first opened up Connectivism and Connective Knowledge 2008, Stephen and I weren’t expecting the response that we received. We had to quickly scramble to organize the course to reflect, first several hundred and then several thousand participants. And the term massive open online course (MOOC) was born. We approached Dave Cormier to help us run the course, especially the live sessions.

Since that first course, we’ve run almost a dozen open courses with over 10,000 participants. I’ve often had the pleasure of meeting former course participants at conferences or, increasingly, other open online courses. It has been a great learning experience for me.

I’ve never really thought about “who are these courses for”. I always figured that they are for whoever decides to show up. Similarly, when I published Knowing Knowledge and the Handbook of Emerging Technologies for Learning, my goal was not to target a particular group, but rather to just open things up and share.

However, after several years of running open courses and meeting colleagues from around the world, I’ve slightly changed my mind as to the ideal participant that I hope will benefit from the course. Many educators are in an environment of embarrassing abundance. As much as I whine about closed journals, I can still access any article I want through the university library. If someone doesn’t want to take an open course with us, there are many others to choose from. Better yet, they can tap into their personal network and setup their own course. Lean on a few personal connections and suddenly you’ve got a reasonably well-organized course with at least some level of technical support. To promote the course, share it on educational listservs, online publications, and blogs.

We can move from idea to open course to promotion to delivery in a fairly short period. Underlying that process, however, is a wealth of support that is easy to overlook. And it’s easy to forget that even being able to run an open course requires a certain degree of privilege. We say “free, open, online” and forget that we host our own servers, have some degree of institutional support, have an existing network that we can tap into to develop and promote the course, etc.

Stephen Downes and I have had several chats about why connectivism seems to have a greater impact in certain parts of the world. For example, I receive a significant amount of correspondence from South America. Why? I haven’t a clue. This past week I was in South Africa and received numerous comments from educators on the value of the Handbook for Emerging Technologies for Learning in helping them to understand how to use technology. Apparently it is being used as a bit of a text at the University of South Africa (UNISA).

It has dawned on me that MOOCs, and openness in general, are not necessarily for those who are trying to work within the existing education system. Open online courses and resources seem to impact those who outside of the traditional education system and in countries that don’t have universities in the “global top 100”. For example, UNISA has over 374,000 students. Harvard has less than 5% of UNISA’s total. Who makes a greater impact in the world? Harvard and other elite universities conduct research that might well alter the course of human history. UNISA and other similar universities alter the lives of single individuals.

Openness in research, education, and scholarship may well have the capacity to alter society and universities. Big changes change big institutions. There is, however, something very gratifying about interacting with individuals, with people who have taken an open course and have been able to improve their teaching practices and their connections with students.

I’m not comfortable with how I’m communicating my thoughts on this. I had a long conversation a few weeks ago with Athabasca University colleagues – Terry Anderson and Jon Dron – about the value of informal publishing in contrast with traditional journals. Publishing an article with a well-regarded journal is important for gaining recognition with peers. Publishing on the open web is important for making an impact with people who might lack the privilege that many of us take for granted. Interestingly, this is often in parts of the world that are forecast to be major regions in future economies. These emerging countries are jostling for identity and place in the world. The ideas they encounter, at least the educators, are those that are open and accessible.

Perhaps it’s time to see the question of openness less from an economic perspective and more from a perspective of contributing, in a very limited but personal way, the shape of education systems in emerging economies. As much as I value positive comments from colleagues, the experience of a teacher shaking my hand and thanking me for posting something online is deeply rewarding.

But it is here that my unease increases. Do I actually think I’m making a difference? Doesn’t it seem very arrogant to proclaim “look, I’m helping people in country XX”? I’m well aware of the prospect of arrogance. I had this feeling several times at UNISA when someone was overly complimentary about my work. On the one hand, I didn’t mind the praise. But on the other hand, I certainly didn’t feel worthy of it. This post holds those same tensions for me: I want to influence those people who don’t have the privilege and access that I, and many of my peers, have. But, in declaring that “I want to help or influence”, I find that unpalatable sense of over-reaching and attempting to inject ideas into areas and regions that should be developing and exporting *their* ideas, not simply importing those from well-meaning, but largely clueless, people from other regions and contexts.

- How old do you think I am?
- Does this outfit make me look fat?
and
- What is knowledge?

(I’m soon going to upgrade “how is connectivism different from constructivism” to the “run” status. Right now it’s more of a “stroll away” question).

Underlying questions of this nature is not the pursuit of an answer, but rather something more personal and value laden. Have you even seen anyone respond with giddy happiness when you over guess their age by 10 years? (well, except maybe a teenager trying to get into a night club). However, intentionally under guessing someone’s age is almost a show of flattery. The questions aren’t the point; the answer is expected to massage an intention.

For a few thousand years, philosophers, theologians, and academics have debated knowledge. They’ve developed multi-syllabic terms like “epistemology” and “hermeneutics” to reflect this exploration. But they certainly haven’t come to an agreement on what “knowledge is”. Which is fine. Stephen Downes and I have explored this topic in every one of the CCK courses. It’s a great developmental question – the value is in the experience of thinking, debating, and understanding the entities that are involved. Rarely do two people agree on the answer. This lack of agreement stems from, I think, the complex interplay of our knowledge definition and our world views (religious, social, political, and so on). Attempts to define knowledge are really attempts to define ourselves. So, tongue-in-cheek: the only time the “what is knowledge” question is worth answering is when the purpose is to expand thinking, not to actually provide a definition.

Over the last few weeks, a rather odd discussion has arisen in relation to massive open online courses (MOOCs). I say odd because at first glance, it’s not a topic that seems worth of much debate At first, I was confused that this topic has generated the level of debate that it has. But, on closer reflection, it makes sense: with MOOCs we are questioning numerous relationships: educator, learner (individual), institution, power, control, and, for that matter, the structure of knowledge and the process of learning. These are high stakes questions. Societies and political systems are built on how these complex issues are perceived.

Changing education is a bit like bathroom renovations gone awry. You go in with the noble goal of replacing a faucet. But once you start, you realize that the sink needs to be replaced because the new faucet doesn’t fit the existing sink. So you remove the sink. Before putting on the new sink, you realize that the drywall needs to be replaced. You remove a panel. And another. Suddenly, the toilet, the bathtub, the floor – everything is fair game for upgrading and replacement.

MOOCs have been our attempt to replace a faucet. But the system is connected. As Martin Weller stated during our ED-MEDIA keynote debate this week, when you hold down one thing, you hold down the adjoining. When you release one thing you release the adjoining. What started as an open online course (CCK08, Alec Couros’ EC&I831) has created a ripple in my thinking. If we see courses as open knowledge spaces where
1) all actors/agents have equal access to information,
2) personal agency is not restricted by pre-planned course structure,
3) democratic practices define participation and rule creation,
4) educators focus on introducing the “big ideas” of a domain and model how they navigate those ideas, and
5) learners, through social sensemaking and wayfinding, orient themselves to complex topics and begin to draw connections between various concepts (big ideas),
then we find ourselves questioning many of the attributes of today’s education system. In the process, we need to address concerns such as David’s emphasis on learner preparedness, the effectiveness of learning in a MOOC in contrast to other learning models, how to structure learning and knowledge spaces in different disciplines and in different topics, and so on.

We need evidence. We need research. Philosophically, the conversation is fun and could go on for years. I’d like to take an empirical approach to expand the possible mode of answering the questions raised in the Wiley/Downes/Cormier/Siemens debate. As I mentioned in the original post announcing our fall MOOC, we are forming a research team to frame and explore the unknowns in open courses. If you’re interested in joining, let me know.

I agree with David’s assertions that MOOCs are effective for learning, that there is a productive place for them in education, and that the name sucks. I disagree with his assertion that the “massive” aspect is irrelevant, that MOOCs are not potentially significant in driving change (that’s a bit of a misstatement of David’s point, but I believe it is in keeping with the spirit of his post).

First – let’s tackle the name. Bryan Alexander and Dave Cormier coined the term at roughly the same time. It stuck because it reflected what was happening with CCK08 – it was open, online, and we had far more learners sign up than we had anticipated. But it seems that everyone hates the term “MOOC”. I have a colleague in Spain who told me that it means “mucus” in Spanish. On the Chronicle site, someone stated that sounds like the word “pig” in Gaelic. David says it’s goofy. Well, we’re agreed then – it’s not a great word. When I first heard the term “blog” I reacted with equal indignation – what a crappy term! But once a term gains a bit of usage and traction, it’s rather hard to change. Beyond agreeing, I don’t have a solution.

David states “Inasmuch as MOOCs seem to be allergic to structure, and go out of their way to avoid structures that would place any kind of requirement (or even moderately strong suggestion) on anyone, they appear to be an extremely poor fit for individuals who are not well prepared academically”. I personally don’t avoid structure and I don’t avoid assessment or grading. I’ve graded students in all three of the CCK offerings. For our upcoming MOOC, several universities are considering offering credit for the course (Georgia Tech and Athabasca U). Both will be building assignment criteria around the course to ensure credibility. Of the complaints David offers, this one surprised me a bit. From what I’ve seen of his presentations, he has been advocating for some level of disaggretation in education. MOOCs follow that trajectory: teaching is open, marking/grading/accreditation happens at an individual institutional levels. Teaching and assessment do not necessarily need to be connected. Learn globally, accredit locally.

However, this isn’t David’s main point here. He suggests that MOOCs are a poor fit for people who aren’t academically prepared. It’s an important consideration. If, in our attempt to open education, we throw barriers in front of learners, we are defeating our goals. I’m not sure how David defines a “prepared learner”. Going back to an Learning Management System example – in 2000 an LMS was a bit foreign, quite clunky, highly technical, and likely only worked well for prepared people with basic tech skills. Today, LMS’ have buried most of that complexity and they are easier to use. People are generally more technically literate as well – most of us have used social networks, social media, and the participative web. It’s easier to use an LMS and learn online when you’re comfortable with the medium. I have a hard time seeing David’s point here – the fact that people don’t have the skills to participate in distributed networks for learning and sensemaking is exactly why we need MOOCs.

The problem David sees is the solution I envision. This has been a sore spot for participants in each of our CCK courses. When the course begins, we inform learners that the process of clarifying confusion and disorientation – sensemaking and wayfinding in complex settings – is the learning. Grappling with pieces that don’t connect and finding a way to connect them is what the course is all about. In the process, learners may move toward a target where knowledge is defined and educators know what learners need to know or they may move more informally in directions that interest them without a goal of accreditation. Many (no idea if it’s most or not) learners that continue in the MOOC seem to settle into the flow of the course and begin to connect pieces. They don’t do this in isolation, however. We have high levels of support in terms of weekly live sessions, Twitter/blogs/The Daily, peer support, and in the learning analytics course we did in January, Dave Cormier started offering a “learner concierge” forum where irritated and confused learners could go with the expectation of getting help.

If the issue that David highlights is one of academic skills – such as when learners don’t have the skills to use a computer or to traverse distributed information, then, yes, he is right – we have a concern that needs to be addressed because people need unique skills to learn in open online courses. But, as with the LMS example, people are learning these skills. It’s not an insurmountable problem. Many schools and colleges teach study skills, critical thinking, and writing skills courses. However, if David states, which I believe he does with his examples of remedial courses, that people can’t learn basic content in this environment, then he is addressing learner capacity, not skills. The only way of addressing this concern is, as he suggests, to run and evaluate courses that target those learners. Even then, if learners don’t do well, we come up against the question of whether the shortcomings are due to learners not having needed skills (which should then be our real focus) or difficulty with subject matter in the distributed network environment.

For me, MOOCs have been wonderful global learning experiences. This article from 2009 details the diversity of open courses – almost half of the participants don’t have English as a first language. That’s significant. I’ve been surprised at the number of learners from developing countries. A MOOC – even when it’s a messy, chaotic, imperfect, frustrating learning experience is still more accessible to many people than a wonderfully designed and well-delivered course. We’ve had over 10 000 participants in courses that we’ve done. Only a fraction of those were active participants. However, I continue to see a growing body of literature on MOOCs, I receive fairly consistent email feedback from people who have found them to be valuable, and I get a fair amount of positive feedback when I attend conferences. As imperfect as MOOCs are, and as much learning as we’re doing trying to figure out how to run them, they are having an impact.

David states, “MOOCs are not a solution to the problem of large and growing demand for higher education for people who are less well prepared”. What would count as a suitable solution to a complex problem such as this? No doubt, it won’t be a single solution. Growing demand for higher education, coupled with calls for increased accountability of the system, presents a complex challenge. What other solutions are being explored that reflect the realities of budget constraints, open content, decreasing faith in higher education as a good investment (at least in the US), and the rapidly growing higher education needs of developing countries such as India? With my involvement with MOOCs, I’m not stating “I have found the answer, follow me!”. Instead, I’m stating “I’m experimenting, join in”.

The concepts we’re exploring with MOOCs – distributed teaching, sub-networks, peer teaching, learner content creation, social networks, new methods of aggregating information, local institution accreditation – are important in reframing the higher education system of the future. MOOCs may or may not have a future. But the ideas we’re playing with and trying to understand will be foundational in any education system in a technology-infused world.

In terms of “massive”, David states: “There are 100s of 1000s of people in these games. “Massively multi-learner” might have made sense if the goal of MOOCs was to serve 100s of 1000s of people”. If I read his comments correctly, he is asking whether MOOCs are about “massive openness” or “massive in terms of numbers of learners”. I’ve always thought it referred to the latter. Massive openness makes no sense. We’ve had anywhere from 500 to 2000+ learners in open courses that I’ve offered with Stephen Downes and Dave Cormier. In those instances, massive means everything. Just as a city can develop advanced services for citizens (such as public transportation) when population density increases, open online course with large numbers of learners have more options than courses with small numbers of learners. We’ve seen courses with different language translations (CCK08’s syllabus was translated into five different languages), different sub-networks (SecondLife, face-to-face get togethers, learner-created live meetings), peer content and technical support, and universities offering credit for students who have participated in courses that we’ve offered. Sub-networks and learner-defined spaces of interaction are a function of the number of participants. If we only had 25 participants, activities and sub-networks wouldn’t make much sense. We need a level of “learner density” in order for the innovation to develop that we’ve witnessed in previous courses.

Social is one of those lovely words that can be added to anything to make it better.

Media? Nah. Social media.

Learning? Nah. Social learning.

Networks? Nah. Social networks.

And the list goes on. It’s almost as if social is a condiment to be added to whatever concept lacks spice and flavour.

Google and Facebook are battling for supremacy at the intersection of information and social. Google states its goal as being one of “organizing the worlds information”. Facebook wants to help you “you connect and share with the people in your life”. Facebook is winning – or so it is commonly thought. But it shouldn’t be. And if Google had the conviction to stick to its information worldview, it would win in the long run. Instead, Google has acquiesced its view and adopted Facebook’s.

If this hypothesis holds true, then humanity has gained astounding intelligence benefits because of social complexity.

But.

Humanity has hit Peak Social – the point at which we can gain no new evolutionary or developmental intellectual advantage from social activity.

Perhaps the most fundamental human trait, after fulfilling biological needs of food, shelter, and procreation, is the desire to impose order on and make sense of the world. We have, historically, activated social attributes in order to manage information complexity. Language is social (Wittgenstein and Vygotsky both attribute the value of language in giving birth to thought). Artifacts – paintings, sculpture, and videos – are simultaneously an expression of individual understanding and a means to enact social and participatory sensemaking.

I believe that humanity’s sensemaking is dominant and social is recessive, activated primarily to serve sensemaking and wayfinding goals and activities. My colleague at Athabasca, Jon Dron, prefers the less radical view that the social is a necessary and sufficient condition for sensemaking. Regardless of ones preferred view, there is an obvious relationship between our capacity to make sense of the world and the need for social networks and systems to do so.

Early information overload indicates a departure from social means of learning and sensemaking. Several hundred years ago (if we set aside geographic constraints of language and libraries, it was several thousand years), humanity crossed a threshold where what was known by humanity could no longer be known by a single person. To combat this deficiency, methods and techniques like indexes and encyclopedias were developed. The thing that pointed to another thing had to grow in encompassing scope. An article in Diderot’s encyclopedia came to stand for a book. Sensemaking broke from social boundaries and moved into the domain of non-human devices.

The development of the telegraph, telephone, and eventually the internet amplified and joined communication and information systems. Suddenly, what was communicated was no longer about information only, but was itself information – captured, stored, and analyzable in a database.

And it is here that we hit peak social. We’ve clustered and sub-clustered our social relations. We’ve fragmented our information sources down to tweets and status updates. A tweet now points to a revolution in the Middle East. Or the IMF chief’s actions in New York. While social is lovely, warm, comfortable, human, we need to start thinking about what’s next in human development. While social will be a huge part of it, it must give way to methods that contribute to, rather than cluster, reduce, sub-network, and silo, sensemaking capacity.

The next evolutionary surge for humanity will be driven by increased reliance on automated systems for information capture and analysis. The infrastructure – the internet and mobile technologies – is already in place. The data being generated is poorly analyzed by individuals (numerous companies are rather eager to do it, but those pesky privacy laws are a problem).

People like Barry Wellman and Caroline Haythornthwaite have contributed significantly to advancing the analysis of the impact of networks on society. Well before Barabasi, Watts, and Strogatz arrived on the network scene, sociologists (and social psychologists) such as Granovetter, Wellman, and Milgram were developing models to understand how people connect. As a result of this work, terms like “six degrees” and “strong/weak ties” and “networked communities” have become mainstream.

With an understanding of how people are connected we can also gain insight into how information flows through a network. I’m sure you’ve seen analysis of the social networks of board of directors at different companies. Valdis Krebs addresses this in Overlapping Networks:

It is usually beneficial to be connected to those who have a good view of what is going on. Information and knowledge is often shared [intentionally or unintentionally] with trusted others, close by. Information leaks and flows, but never too far. Board members who are connected to other highly-aware Board members, have a higher probability of finding out more — but the range is limited.

While networks have always been the backbone structure of society and knowledge, they were situated just underneath the consciousness of most people as the experience of life itself pushed networks to the background. Let me give an example. A farmer in 250 BC would teach his children how to farm (networked learning). The farmer’s family was part of a larger network: religious, agriculture (selling, buying), social, entertainment, and so on. Their position in society was determined by their networked background such as: who they knew, who their parents knew, their connection to community leaders, and their involvement in the army. However, the experience of being part of a network was not fully conscious or even explicit. What mattered was who you knew and your role in society – but the daily experience was likely not an explicit one of “I’m connected by 3 degree to person X”. The key here is the explicit, rather than experiential, encounter of networks.

Today, in contrast, our networks are explicit in tools like Facebook, Twitter, email, and LinkedIn. Most of these services give users the ability to analyze how they are connected to others. We are very aware of how we are connected. Even the act of connection forming requires explicit activity from a person : “Follow X” or “Accept friend request from X”. The online formation of networks is more directive than the offline experience. This morning, while at a local Chamber of Commerce breakfast, I met several people that I’d known by name but had not met before. I followed the social protocol of introduction, shaking hands, and polite conversation. While a connection was made, it wasn’t explicit and didn’t carry with it any lingering sense of connectedness. When I follow someone on Facebook or Twitter, the connection seems more real, more intentional.

The daily reality of being connected naturally raises questions about influence of an individual within a network and how information flows within that system. Klout analyzes influence. SNAPP analyzes the social networks that underpin interaction in a learning management system. Researchers can gain insight into how information flows through a company by email analysis. The prevalence of social network tools and the attention now devoted to analyzing the shape and attributes of those networks – and the evaluation of how information flows – overlooks an important question: Why? Why does information flow as it does? Why does a person decide to share information with her network?

Networks can be analyzed quantitatively to determine connectedness, structural holes/folds, degrees of separation, centrality, small worlds, and so on. I’m interested in the qualitative aspects of information flow. Why did you decide to post on your friend’s Facebook wall? Why did you decide to retweet a resource? Why did members of your network decide to retweet your comment?

What are the qualitative aspects of information objects that determine its likelihood of being shared or amplified within a network?

Let’s consider three elements that are involved in addressing the question of “why does information flow” in a network:1. The individual. If someone has a large following on Twitter, their message will reach larger numbers of people. However, this is an analysis of how information flows – it flows better when more people hear it. Again, why did the person decide to post the message in the first place? Or, for that matter, how did the person get to have many followers? Artists like Lady Gaga acquire followers simply by fame. They don’t provide much insight into why people have different numbers of followers on Twitter. It’s a spillover of their fame in other spaces.

Let’s look at someone like Alec Couros on Twitter. He has 12000 followers. I have 7400 followers. He has posted over 55000 tweets (wow!). I’ve posted 8300. What are the activities of a person like Alec that give him the higher follower count? i.e. – qualitatively, how does Alec differ from others in his activities on Twitter? Does he have more followers because he posts more often? Because he is talented at engaging with individuals? Is it because he replies to more of his followers than I do and that’s why they continue to follow him? Does he participate in more network sub-clusters (such as the #edtech, #phd, or #learn hashtag communities)? Maybe he’s just a nicer person than I am and people pick that up in his tweets.

Clearly, the activities of an individual plays a role in why information flows…

2. The Context. Context also influences why information spreads. For example, Sohaib Athar live tweeted the raid on Osama bin Laden’s Abbottabad compound. His followers shot up to over 104000 within days. The Bronx Zoos Cobra has over 240000 followers as the voice of the escaped (since captured) Bronx Zoo Cobra. During BP’s Gulf oil spill, the BP Global PR account on Twitter gathered over 170000 followers as it mocked BP. On a far smaller scale, I did a presentation at TEDxNYED (video here) in 2010 and added over 100 followers in one day. TEDxNYED served as a bridge into the K-12 community that I’m not very involved with. I’d love to see an analysis of follower counts in different communities. The K-12 community seems better connected and more active on Twitter than the higher education community. Does a K-12 twitterer have a better chance for quickly building followers than someone in a poorly connected field?

3. The Message. This is really the heart of what I’m trying to understand. What are the qualitative attributes of a message that influence why it is shared. Two attributes come to mind readily:
-Relevance – a tweet about something happening today is more valuable than tweeting that Pearl Harbour was attacked.
-Resonance – this is a complex/fuzzy concept that I haven’t fully wrapped my head around but I know it’s important. When someone posts a link or comment on Twitter, and it resonates with me (fears, interests, beliefs), the prospect of retweeting is increased.

Let’s look at a simple coding scheme of what types of messages people post on Twitter:

a) to express agreement
b) to express outrage
c) humour
d) social grooming (I have an iPad, I met person X today, I went for a run, I ate fruit for breakfast)
e) self-promote
f) raise awareness – general information sharing about topics that might be be relevant for network members

Looking at that list – what would you add?

Suggestion: Let’s create a coding scheme (we can do inter-rater validation if it makes people happy) on why things get posted to and shared on social media (Twitter, FB seem the best candidates).. If we have a coding scheme, we can randomly analyze the posting habits of people on Twitter (i.e. who is completely self-absorbed by self-referential tweets). No doubt, the coding process would be better if it was automated (that way we could evaluate the impact of RTs) – sentiment analysis is a big area of focus for social media firms. Not only are media firms interested in who is talking about GM or BP, but what are the emotions behind posts on Twitter/FB?

Educators are paying attention to social media. The surface level network infatuation won’t generate much value in the long run. Getting at the qualitative aspects of why information flows through networks is a more lucrative direction to consider in transitioning social media use for self and network awareness.

We just wrapped up our third offering of the Connectivism and Connective Knowledge Open course: CCK11 (readings, recordings, and archives of the Daily are available on the site).

The course is offered as part of the Certificate in Emerging Technologies for Learning that I developed while at University of Manitoba. Twenty students (the max allowed for the course) enrolled in the for-credit version of CCK11. This means they had assignment requirements including two essays, a concept map, and a final project. The final project has been a requirement of all our previous offerings – and has produced some great resources, including the rather popular Networked Student video by Wendy Drexler – now approaching 100 000 views.

Marking assignments can be rewarding as it requires sustained time and focus. Due to quantity, I usually skim most blog posts and articles posted during a CCK course. Marking, however, requires time and focus, which makes it a good learning experience for me. For example, this presentation – Institutional and/vs Networked Learning – in CCK11 raises an important concern around self-directed learning. Leah makes the statement that the expectations of open courses goes “way beyond self-directed learning”. At first this statement took me back a bit. After all, open courses are require learner autonomy and self-directedness. We want learners to get comfortable with personal wayfinding through complex topics and to utilize tools to splice activity streams in order to fulfill personal learning goals.

After a bit of time thinking about CCK requiring “more than self-directed learning”, it dawned on me that Leah had identified an important distinction. Self-directed learning has a long research and philosophical tradition. Malcolm Knowles figues prominently in discussions, but roots go back to Dewey, and even further, to humanist philosophers.

While connectivism begins with the individual, it stresses the growth of connections and connectedness in learning and knowledge. Self-directed learning explains the attributes of learners who learn at their own pace and interest. Is that sufficient to describe our knowledge needs today? I don’t think so.

When faced with learning in complex environments, what we need is something more like network-directed learning – learning that is shaped, influenced, and directed by how we are connected to others. Instead of sensemaking in isolation, we rely on social, technological, and informational networks to direct our activities.

With MOOCs, we emphasize that early course experiences tend to be overwhelming and chaotic. After all, learners face hundreds of introductions, blogs posts, and reading resources, in addition to dozens of new tools and technologies. As the course progresses, small sub-networks form based on shared interests and goals. Learners also gather in various social spaces that we as facilitators don’t create (Facebook was common in CCK11 as was SecondLife) and in language specific forums – a key requirement with global courses.

To address the information and social complexity of open courses, learners need to be network-directed, not self-directed learners. Social networks serve to filter and amplify important concepts and increase the diversity of views on controversial topics. This transition is far broader than only what we’ve experienced in open courses – the need for netwok-centric learning and knowledge building is foundational in many careers today. For example, the discovery of the corona virus (SARS) was achieved through a global distributed research network. New technologies are increasingly assemblies of innovations that often span millennia – a process that was wonderfully covered by William Rosen in The Most Powerful Idea in the World: A Story of Steam, Industry, and Invention . To be competent, to be creative, to be adaptable, requires that we are connected.

Most importantly network-directed learning is not a “crowd sourcing” concept. Crowd sourcing involves people creating things together. Networks involve connected specialization – namely we are intelligent on our own and we amplify that intelligence when we connect to others. Connectedness – in this light – consists of increasing, not diminishing, the value of the individual.